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A study on software fault prediction techniques
Artificial Intelligence Review, 2017Software fault prediction aims to identify fault-prone software modules by using some underlying properties of the software project before the actual testing process begins.
Santosh S. Rathore, Sandeep Kumar
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Software fault prediction tool
Proceedings of the 19th international symposium on Software testing and analysis, 2010We have developed an interactive tool that predicts fault likelihood for the individual files of successive releases of large, long-lived, multi-developer software systems. Predictions are the result of a two-stage process: first, the extraction of current and historical properties of the system, and second, application of a negative binomial ...
Thomas J. Ostrand, Elaine J. Weyuker
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Predicting and Classifying Software Faults
Proceedings of the 2019 7th International Conference on Computer and Communications Management, 2019In the field of software engineering, the detection of fault in the software has become a major topic to explore. With the help of data mining and machine learning approaches, this paper aims to denote whether a software is fault prone or not. In order to accomplish that this paper gives importance to compare between different machine learning ...
Shamse Tasnim Cynthia, Shamim H. Ripon
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Class level fault prediction using software clustering
2013 28th IEEE/ACM International Conference on Automated Software Engineering (ASE), 2013Defect prediction approaches use software metrics and fault data to learn which software properties associate with faults in classes. Existing techniques predict fault-prone classes in the same release (intra) or in a subsequent releases (inter) of a subject software system.
Giuseppe Scanniello +4 more
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Software fault prediction using firefly algorithm
International Journal of Intelligent Engineering Informatics, 2018The software fault prediction (SFP) literature has shown an immense growth of the research studies involving the artificial neural network (ANN) based fault prediction models. However, the default gradient descent back propagation neural networks (BPNNs) have a high risk of getting stuck in the local minima of the search space.
Ishani Arora, Anju Saha
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Journal of Systems and Software, 1995
Abstract Cost-effective and timely software development methods are essential today as software costs and backlogs escalate while applications are developed in rapidly changing environments. Focusing testing efforts on those portions of the code with the largest number of faults can reduce development costs and time, but requires prediction of the ...
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Abstract Cost-effective and timely software development methods are essential today as software costs and backlogs escalate while applications are developed in rapidly changing environments. Focusing testing efforts on those portions of the code with the largest number of faults can reduce development costs and time, but requires prediction of the ...
openaire +1 more source